Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Incomplete Multimodal Federated Learning via Masking and Contrasting Prototypes.

IEEE transactions on neural networks and learning systems·2026

Same author

Modeling Enzyme Temperature Stability from Sequence Segment Perspective.

Journal of chemical information and modeling·2025

Same author

Robust Multivariate Time Series Forecasting Against Intraseries and Interseries Transitional Shift.

IEEE transactions on neural networks and learning systems·2025

Same author

Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

Distilling the Unknown to Unveil Certainty.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

Variational Hierarchical N-BEATS Model for Long-Term Time-Series Forecasting.

IEEE transactions on neural networks and learning systems·2025

Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026

Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026

Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026

Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 26, 2025

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences.

Wei Wang, Longbing Cao

IEEE Transactions on Neural Networks and Learning Systems

|October 10, 2022

Summary

This summary is machine-generated.

This study introduces EINSP, a novel method for negative sequence analysis (NSA). EINSP discovers actionable negative sequential patterns (NSPs) by quantifying explicit and implicit relations, offering diverse and significant insights from sequential data.

More Related Videos

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Related Experiment Videos

Last Updated: Aug 26, 2025

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Data Mining
Sequential Data Analysis
Pattern Recognition

Background:

Real-world events generate sequential data, necessitating analysis of both occurring and non-occurring patterns.
Traditional negative sequence analysis (NSA) often yields redundant and non-actionable negative sequential patterns (NSPs).

Purpose of the Study:

To develop an actionable negative sequential pattern (NSP) discovery method.
To address limitations in existing NSA by focusing on significant, diverse, and informative NSPs.

Main Methods:

Introduced EINSP, a method utilizing a determinantal point process (DPP)-based graph representation for NSPs.
Quantified explicit occurrence and implicit non-occurrence-based element and pattern relations.
Modeled and measured both explicit and implicit relations to represent couplings between NSP items, elements, and patterns.

Main Results:

EINSP effectively discovers significant and diverse NSPs, representing the entire NSP set.
The method quantifies actionable NSPs based on statistical significance, diversity, and relation strength.
Demonstrated effectiveness in terms of complexity, coverage, pattern diversity, and relation strength.

Conclusions:

EINSP provides a novel approach to actionable NSP discovery in negative sequence analysis.
The DPP-based graph representation and relation quantification offer significant contributions to the field.
EINSP enables more informative and actionable insights from sequential data compared to traditional methods.